Title :
Resilient K-d Trees: K-Means in Space Revisited
Author :
Gieseke, Fabian ; Moruz, Gabriel ; Vahrenhold, Jan
Author_Institution :
Tech. Univ. Dortmund, Dortmund, Germany
Abstract :
We develop a k-d tree variant that is resilient to a pre-described number of memory corruptions while still using only linear space. We show how to use this data structure in the context of clustering in high-radiation environments and demonstrate that our approach leads to a significantly higher resiliency rate compared to previous results.
Keywords :
data analysis; pattern clustering; tree data structures; data structure; high radiation environment; linear space; memory corruption; resiliency rate; resilient k-d tree; revisited space; clustering; k-d tree; k-means; resilient algorithm;
Conference_Titel :
Data Mining (ICDM), 2010 IEEE 10th International Conference on
Conference_Location :
Sydney, NSW
Print_ISBN :
978-1-4244-9131-5
Electronic_ISBN :
1550-4786
DOI :
10.1109/ICDM.2010.94
